Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "109"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 109 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 109, Node N10:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2460009 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.652636 15.166957 13.049923 13.609510 7.344909 8.873288 0.519635 2.455696 0.0620 0.0344 0.0159 nan nan
2460008 digital_ok 100.00% 95.03% 100.00% 0.00% - - 14.043323 18.551006 14.263774 14.967222 6.601021 7.796471 4.291776 5.608137 0.0904 0.0385 0.0344 nan nan
2460007 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.456155 13.902558 11.157248 11.703475 5.945752 7.237270 1.172997 2.627673 0.0585 0.0354 0.0143 nan nan
2459999 digital_ok 0.00% 98.58% 98.75% 0.00% - - nan nan nan nan nan nan nan nan 0.3093 0.2984 0.2314 nan nan
2459998 digital_ok 100.00% 100.00% 100.00% 0.00% - - 8.888857 11.721620 9.550884 9.899216 8.038066 10.254914 0.400792 2.029686 0.0500 0.0321 0.0107 nan nan
2459997 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.742378 12.801277 10.126323 10.644197 7.770629 9.670089 1.427947 3.369527 0.0583 0.0341 0.0146 nan nan
2459996 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.829615 13.776997 12.716345 13.041087 7.329061 9.303214 0.165906 1.242185 0.0458 0.0330 0.0076 nan nan
2459995 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.030250 14.003734 11.794269 12.236419 8.117560 9.523302 0.090224 1.211270 0.0514 0.0365 0.0080 nan nan
2459994 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.582700 13.594487 10.175303 10.715619 7.840562 9.581409 0.773749 1.739334 0.0437 0.0337 0.0059 nan nan
2459993 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.732482 12.688889 9.452406 9.920170 10.245232 10.957080 0.508156 2.402364 0.0314 0.0299 0.0021 nan nan
2459991 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.570970 15.825191 10.017683 10.505258 9.241072 10.783019 -0.022185 1.016421 0.0280 0.0332 0.0035 nan nan
2459990 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.193069 13.062570 9.808130 10.200391 9.150500 11.078704 -0.127326 0.792105 0.0284 0.0339 0.0037 nan nan
2459989 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.973677 13.207453 8.730628 9.331312 8.071392 9.288843 -0.365846 0.495675 0.0276 0.0293 0.0016 nan nan
2459988 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.955217 15.465142 10.118855 10.483080 10.853821 13.251484 -0.151177 0.729702 0.0274 0.0293 0.0017 nan nan
2459987 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.831044 12.968804 9.819990 10.356207 6.427632 7.987203 0.311290 2.022822 0.0263 0.0302 0.0024 nan nan
2459986 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.326254 15.891519 10.753213 11.171855 9.448144 11.279762 5.289769 9.730889 0.0262 0.0293 0.0020 nan nan
2459985 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.293807 14.383510 9.968577 10.415000 7.281663 8.630265 0.644563 2.044443 0.0261 0.0287 0.0017 nan nan
2459984 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.759539 13.805812 10.342540 10.793222 9.441090 12.122993 1.849621 2.911624 0.0266 0.0293 0.0020 nan nan
2459983 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.309598 13.149898 9.376724 9.686304 9.294923 11.135537 2.500249 6.154492 0.0276 0.0303 0.0020 nan nan
2459982 digital_ok 100.00% 100.00% 100.00% 0.00% - - 8.933842 11.039730 8.390020 8.715479 4.562910 5.282956 2.372034 3.207024 0.0281 0.0261 0.0018 nan nan
2459981 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.804343 12.433442 10.539915 10.847937 10.508809 12.382365 -0.000581 1.151911 0.0283 0.0260 0.0020 nan nan
2459980 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.626946 11.982574 9.476532 9.920581 9.118643 10.842015 5.134341 5.420350 0.0288 0.0264 0.0020 nan nan
2459979 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.995669 12.526124 8.773113 9.275485 9.005878 10.138899 0.312308 1.327067 0.0300 0.0262 0.0019 nan nan
2459978 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.101255 12.721996 9.529859 9.984495 9.402739 10.986861 -0.263714 1.125960 0.0275 0.0257 0.0017 nan nan
2459977 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.424825 13.446329 9.361321 9.848216 9.299212 11.336504 0.579950 2.100385 0.0287 0.0262 0.0021 nan nan
2459976 digital_ok 100.00% 0.00% 100.00% 0.00% - - 3.761148 12.948232 7.586861 10.251845 3.771131 10.875317 -0.541928 1.301880 0.5082 0.0300 0.3524 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 109: 2460009

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 15.166957 11.652636 15.166957 13.049923 13.609510 7.344909 8.873288 0.519635 2.455696

Antenna 109: 2460008

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 18.551006 18.551006 14.043323 14.967222 14.263774 7.796471 6.601021 5.608137 4.291776

Antenna 109: 2460007

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 13.902558 10.456155 13.902558 11.157248 11.703475 5.945752 7.237270 1.172997 2.627673

Antenna 109: 2459999

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 109: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 11.721620 8.888857 11.721620 9.550884 9.899216 8.038066 10.254914 0.400792 2.029686

Antenna 109: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 12.801277 9.742378 12.801277 10.126323 10.644197 7.770629 9.670089 1.427947 3.369527

Antenna 109: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 13.776997 10.829615 13.776997 12.716345 13.041087 7.329061 9.303214 0.165906 1.242185

Antenna 109: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 14.003734 11.030250 14.003734 11.794269 12.236419 8.117560 9.523302 0.090224 1.211270

Antenna 109: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 13.594487 10.582700 13.594487 10.175303 10.715619 7.840562 9.581409 0.773749 1.739334

Antenna 109: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 12.688889 11.732482 12.688889 9.452406 9.920170 10.245232 10.957080 0.508156 2.402364

Antenna 109: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 15.825191 12.570970 15.825191 10.017683 10.505258 9.241072 10.783019 -0.022185 1.016421

Antenna 109: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 13.062570 13.062570 10.193069 10.200391 9.808130 11.078704 9.150500 0.792105 -0.127326

Antenna 109: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 13.207453 13.207453 9.973677 9.331312 8.730628 9.288843 8.071392 0.495675 -0.365846

Antenna 109: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 15.465142 15.465142 11.955217 10.483080 10.118855 13.251484 10.853821 0.729702 -0.151177

Antenna 109: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 12.968804 9.831044 12.968804 9.819990 10.356207 6.427632 7.987203 0.311290 2.022822

Antenna 109: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 15.891519 15.891519 12.326254 11.171855 10.753213 11.279762 9.448144 9.730889 5.289769

Antenna 109: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 14.383510 14.383510 11.293807 10.415000 9.968577 8.630265 7.281663 2.044443 0.644563

Antenna 109: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 13.805812 10.759539 13.805812 10.342540 10.793222 9.441090 12.122993 1.849621 2.911624

Antenna 109: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 13.149898 10.309598 13.149898 9.376724 9.686304 9.294923 11.135537 2.500249 6.154492

Antenna 109: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 11.039730 8.933842 11.039730 8.390020 8.715479 4.562910 5.282956 2.372034 3.207024

Antenna 109: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 12.433442 12.433442 9.804343 10.847937 10.539915 12.382365 10.508809 1.151911 -0.000581

Antenna 109: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 11.982574 11.982574 9.626946 9.920581 9.476532 10.842015 9.118643 5.420350 5.134341

Antenna 109: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 12.526124 9.995669 12.526124 8.773113 9.275485 9.005878 10.138899 0.312308 1.327067

Antenna 109: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 12.721996 12.721996 10.101255 9.984495 9.529859 10.986861 9.402739 1.125960 -0.263714

Antenna 109: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 13.446329 10.424825 13.446329 9.361321 9.848216 9.299212 11.336504 0.579950 2.100385

Antenna 109: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
109 N10 digital_ok nn Shape 12.948232 12.948232 3.761148 10.251845 7.586861 10.875317 3.771131 1.301880 -0.541928
In [ ]: